Unsupervised machine learning (ML) with Self-Organizing Maps (SOM) was applied to a 3D seismic reflection survey in an area in Northwestern Colombia, South America to better visualize the reservoir of a newly discovered field. This technology encom- passes multiple volumes of seismic attributes which are combined into a single volume of multi-attribute seismic samples. With ML, these data are classified with the statistics of sample interval res- olution. It is expected that classified seismic samples associated with many SOM winning neurons can be interpreted as single depositional environments with unique rock properties associated with that winning neuron (Roden et al., 2015; Roden and Chen, 2017). Seismic interpretation skills are vital to this process. Other winning neurons, for example, are recognized as assemblages associated with acquisition footprints and others with seismic noise assemblages. In this study, the SOM results were calibrated to wells that have been drilled to date. Geobodies from these winning neurons, which tied to productive intervals registered in these wells, were then visualized for their thicknesses and areal extents within the reservoir field. A workflow is presented which includes data conditioning, finding the best combination of attributes for ML classification aided by Principal Component Analysis, unsupervised ML through SOM multi-attribute seismic sample training and then survey classification in the zone of interest and, finally, geobodies created from classified samples of selected winning neurons, Vizualization of these results are outlined in this paper. The result are potential reservoir estimates calculated through geobodies which have been interpreted with unsupervised ML classifications.